SoroushVahidi/scidocs huggingface dataset:
Tight bounds on quantum searching
(1996)

We provide a tight analysis of Grover’s recent algorithm for quantum database searching. We give a simple closed-form formula for the probability of success after any given number of iterations of the algorithm. This allows us to determine the number of iterations necessary to achieve almost certainty of finding the answer. Furthermore, we analyse the behaviour of the algorithm when the element to be found appears more than once in the table and we provide a new algorithm to find such an element even when the number of solutions is not known ahead of time. Using techniques from Shor’s quantum factoring algorithm in addition to Grover’s approach, we introduce a new technique for approximate quantum counting, which allows to estimate the number of solutions. Finally we provide a lower bound on the efficiency of any possible quantum database searching algorithm and we show that Grover’s algorithm nearly comes within a factor 2 of being optimal in terms of the number of probes required in the table.
Algorithms for Quantum Computation: Discrete Logarithms and Factoring
(1994)

A computer is generally considered to be a universal computational device; i.e., it is believed able to simulate any physical computational device with a cost in computation time of at most a polynomial factol: It is not clear whether this is still true when quantum mechanics is taken into consideration. Several researchers, starting with David Deutsch, have developed models for quantum mechanical computers and have investigated their computational properties. This paper gives Las Vegas algorithms for finding discrete logarithms and factoring integers on a quantum computer that take a number of steps which is polynomial in the input size, e.g., the number of digits of the integer to be factored. These two problems are generally considered hard on a classical computer and have been used as the basis of several proposed cryptosystems. (We thus give the first examples of quantum cryptanulysis.)
A Study of Cardiovascular Disease Prediction Models Using Discriminant Analysis
(2013)

Cardiovascular disease is a condition of interest to clinicians around the world and is one of the chief causes of death. In Korea, cardiovascular disease is the second largest cause of death following cancer, and is classified as one of the four major diseases, making an early diagnosis of cardiovascular disease essential for proper and timely treatment. Although the risk of cardiovascular disease is recognized in Korea, there is a lack of quality studies regarding the disease. In the present study, a prediction model was developed which allows for the calculation of the risk of cardiovascular disease among Korean patients. The prediction model for cardiovascular disease was developed using a discriminant analysis based on the data set of Korean National Health and Nutrition Examinations Survey V (KNHANES V).
High-performance polyline intersection based spatial join on GPU-accelerated clusters
(2016)

The rapid growing volumes of spatial data have brought significant challenges on developing high-performance spatial data processing techniques in parallel and distributed computing environments. Spatial joins are important data management techniques in gaining insights from large-scale geospatial data. While several distributed spatial join techniques based on spatial partitions have been implemented on top of existing Big Data systems, they are not capable of natively exploiting massively data parallel computing power provided by modern commodity Graphics Processing Units (GPUs). In this study, as an important component of our research initiative in developing high-performance spatial join techniques on GPUs, we have designed and implemented a polyline intersection based spatial join technique that is capable of exploiting massively data parallel computing power on GPUs. The proposed polyline intersection based spatial join technique is integrated into a customized lightweight distributed execution engine that natively supports spatial partitions. We empirically evaluate the performance of the proposed spatial join technique on both a standalone GPU-equipped workstation and Amazon EC2 GPU-accelerated clusters and demonstrate its high performance when comparing with the state-of-the-art.
The Case for Holistic Data Integration
(2016)

Current data integration approaches are mostly limited to few data sources, partly due to the use of binary match approaches between pairs of sources. We thus advocate for the development of more holistic, clustering-based data integration approaches that scale to many data sources. We outline different use cases and provide an overview of initial approaches for holistic schema/ontology integration and entity clustering. The discussion also considers open data repositories and socalled knowledge graphs.
Comprehensive review of epidemiology, scope, and impact of spinal pain.
(2009)

Persistent pain interfering with daily activities is common. Chronic pain has been defined in many ways. Chronic pain syndrome is a separate entity from chronic pain. Chronic pain is defined as, "pain that persists 6 months after an injury and beyond the usual course of an acute disease or a reasonable time for a comparable injury to heal, that is associated with chronic pathologic processes that cause continuous or intermittent pain for months or years, that may continue in the presence or absence of demonstrable pathologies; may not be amenable to routine pain control methods; and healing may never occur." In contrast, chronic pain syndrome has been defined as a complex condition with physical, psychological, emotional, and social components. The prevalence of chronic pain in the adult population ranges from 2% to 40%, with a median point prevalence of 15%. Among chronic pain disorders, pain arising from various structures of the spine constitutes the majority of the problems. The lifetime prevalence of spinal pain has been reported as 54% to 80%. Studies of the prevalence of low back pain and neck pain and its impact in general have shown 23% of patients reporting Grade II to IV low back pain (high pain intensity with disability) versus 15% with neck pain. Further, age related prevalence of persistent pain appears to be much more common in the elderly associated with functional limitations and difficulty in performing daily life activities. Chronic persistent low back and neck pain is seen in 25% to 60% of patients, one-year or longer after the initial episode. Spinal pain is associated with significant economic, societal, and health impact. Estimates and patterns of productivity losses and direct health care expenditures among individuals with back and neck pain in the United States continue to escalate. Recent studies have shown significant increases in the prevalence of various pain problems including low back pain. Frequent use of opioids in managing chronic non-cancer pain has been a major issue for health care in the United States placing a significant strain on the economy with the majority of patients receiving opioids for chronic pain necessitating an increased production of opioids, and escalating costs of opioid use, even with normal intake. The additional costs of misuse, abuse, and addiction are enormous. Comorbidities including psychological and physical conditions and numerous other risk factors are common in spinal pain and add significant complexities to the interventionalist's clinical task. This section of the American Society of Interventional Pain Physicians (ASIPP)/Evidence-Based Medicine (EBM) guidelines evaluates the epidemiology, scope, and impact of spinal pain and its relevance to health care interventions.
Design of a Wheelchair with Legs for People with Motor Disabilities
(1995)

Low cost. For example, golf carts, outdoor chairs and special purpose sand buggies. wheelchair. AbstractA proof-of-concept prototype wheelchair with legs for people with motor disabilities is proposed, with the objective of demonstrating the feasibility of a completely new approach to mobility. Our prototype system consists of a chair equipped with wheels and legs, and is capable of traversing uneven terrain and circumventing obstacles. The important design considerations, the system design and analysis, and an experimental prototype of a chair are discussed. The results from the analysis and experimentation show the feasibility of the proposed concept and its advantages.
Uneasy lies the head that wears the crown: the link between guilt proneness and leadership.
(2012)

We propose that guilt proneness is a critical characteristic of leaders and find support for this hypothesis across 3 studies. Participants in the first study rated a set of guilt-prone behaviors as more indicative of leadership potential than a set of less guilt-prone behaviors. In a follow-up study, guilt-prone participants in a leaderless group task engaged in more leadership behaviors than did less guilt-prone participants. In a third, and final, study, we move to the field and analyze 360° feedback from a group of young managers working in a range of industries. The results indicate that highly guilt-prone individuals were rated as more capable leaders than less guilt-prone individuals and that a sense of responsibility for others underlies the positive relationship between guilt proneness and leadership evaluations.
Implementing it Service Management: A Case Study Focussing on Critical Success Factors
(2009)

Queensland Health, a large Australian government agency, implemented a centralised IT service management model based on the ITIL framework. This paper presents an in-depth case study of the implementation. It sheds light on the challenges and breakthroughs, confirms a set of factors that contributed to the project’s success and offers a learning opportunity for other organisations. The study indicates that the commitment of senior management is crucial to the project’s success as is a project champion and the recognition of the need for an appropriate change management strategy to transform the organisational culture to a service-oriented focus. Maintaining close and forthright relationships with multiple vendors facilitates technology transfer to in-house staff while a benefits realisation plan is a valuable tool for tracking and communicating tangible and intangible project benefits to the project stakeholders. An effective project governance and execution process further contributes to the implementation success.
Simplification of Example Sentences for Learners of Japanese Functional Expressions
(2016)

Learning functional expressions is one of the difficulties for language learners, since functional expressions tend to have multiple meanings and complicated usages in various situations. In this paper, we report an experiment of simplifying example sentences of Japanese functional expressions especially for Chinese-speaking learners. For this purpose, we developed “Japanese Functional Expressions List” and “Simple Japanese Replacement List”. To evaluate the method, we conduct a small-scale experiment with Chinese-speaking learners on the effectiveness of the simplified example sentences. The experimental results indicate that the simplified sentences are helpful in learning Japanese functional expressions.
Algorithmics of Matching Under Preferences
(2013)

• Page 17, line 13: “constrast” −→ “contrast”. (Due to Mechthild Opperud.) • Page 21, line 17: “attributes” −→ “attributed”. • Page 21, lines -4 to -3: change “pairs in which either (i) ri is unassigned if she is unassigned in both M and M ′, or (ii)” to “pairs obtained as follows: for each resident ri, ri is unassigned if she is unassigned in both M and M ′, otherwise”. • Page 21, line -1: “join” −→ “meet”. (Due to Didac Busquets.) • Page 22, line 1: “meet” −→ “join”. (Due to Didac Busquets.) • Page 23, line 4: add “In an sm instance, any matching is automatically assumed to have size n.”
SNAP, Small-world Network Analysis and Partitioning: An open-source parallel graph framework for the exploration of large-scale networks
(2008)

We present SNAP (small-world network analysis and partitioning), an open-source graph framework for exploratory study and partitioning of large-scale networks. To illustrate the capability of SNAP, we discuss the design, implementation, and performance of three novel parallel community detection algorithms that optimize modularity, a popular measure for clustering quality in social network analysis. In order to achieve scalable parallel performance, we exploit typical network characteristics of small-world networks, such as the low graph diameter, sparse connectivity, and skewed degree distribution. We conduct an extensive experimental study on real-world graph instances and demonstrate that our parallel schemes, coupled with aggressive algorithm engineering for small-world networks, give significant running time improvements over existing modularity-based clustering heuristics, with little or no loss in clustering quality. For instance, our divisive clustering approach based on approximate edge betweenness centrality is more than two orders of magnitude faster than a competing greedy approach, for a variety of large graph instances on the Sun Fire T2000 multicore system. SNAP also contains parallel implementations of fundamental graph-theoretic kernels and topological analysis metrics (e.g., breadth-first search, connected components, vertex and edge centrality) that are optimized for small- world networks. The SNAP framework is extensible; the graph kernels are modular, portable across shared memory multicore and symmetric multiprocessor systems, and simplify the design of high-level domain-specific applications.
Advances in Very Deep Convolutional Neural Networks for LVCSR
(2016)

Very deep CNNs with small 3 × 3 kernels have recently been shown to achieve very strong performance as acoustic models in hybrid NN-HMM speech recognition systems. In this paper we investigate how to efficiently scale these models to larger datasets. Specifically, we address the design choice of pooling and padding along the time dimension which renders convolutional evaluation of sequences highly inefficient. We propose a new CNN design without timepadding and without timepooling, which is slightly suboptimal for accuracy, but has two significant advantages: it enables sequence training and deployment by allowing efficient convolutional evaluation of full utterances, and, it allows for batch normalization to be straightforwardly adopted to CNNs on sequence data. Through batch normalization, we recover the lost peformance from removing the time-pooling, while keeping the benefit of efficient convolutional evaluation. We demonstrate the performance of our models both on larger scale data than before, and after sequence training. Our very deep CNN model sequence trained on the 2000h switchboard dataset obtains 9.4 word error rate on the Hub5 test-set, matching with a single model the performance of the 2015 IBM system combination, which was the previous best published result.
Non-orthogonal Multiple Access in a Downlink Multiuser Beamforming System
(2013)

In this paper, we propose a non-orthogonal multiple access-based multiuser beamforming (NOMA-BF) system designed to enhance the sum capacity. In the proposed NOMA-BF system, a single BF vector is shared by two users, so that the number of supportable users can be increased. However, sharing a BF vector leads to interference from other beams as well as from the other user sharing the BF vector. Therefore, to reduce interference and improve the sum capacity, we additionally propose a clustering and power allocation algorithm. This clustering algorithm, which selects two users with high correlation and a large gain-difference between their channels, can reduce the interference from other beams and from the other user as well. Furthermore, power allocation ensures that each user's transmit power is allocated so as to maximize the sum capacity. Numerical results verify that the proposed NOMA-BF system improves the sum capacity, compared to the conventional multiuser BF system.
On Plant Responses to D-Amino Acids Features of Growth , Root Behaviour and Selection for Plant Transformation
(2016)

Amino acids have been regarded as potential plant nitrogen sources for more than a century. Some amino acids have been shown to support growth while others have growth-retarding effects. The D-isomers with notably adverse effects on plants’ growth and development include D-serine and D-alanine. Recently, D-serine has been recognised as an endogenous ligand of receptor channels mediating calcium fluxes in plants, but otherwise little is known about endogenous roles of D-amino acids in plants. In the studies underlying this thesis, the negative responses to D-serine and D-alanine were converted to positive effects in Arabidopsis thaliana (Arabidopsis) plants by introducing either of two D-amino acid-metabolising enzymes. Transgenic Arabidopsis lines expressing either the D-serine dehydratase (dsdA) gene from Escherichia coli or the D-amino acid oxidase (DAO1) gene from Rhodotorula gracilis grew with otherwise toxic D-amino acids as the sole nitrogen source. I also expressed a transporter specific for D-amino acids, which further increased the transgenic plants’ growth with D-serine as sole nitrogen source. Hence, both assimilation and uptake restrictions can limit plant growth on D-amino acids. The growth of transgenic lines on D-serine or D-alanine provides an unambiguous and highly visible phenotype, which is essential for a selectable marker. Thus, expressing of either the dsdA or DAO1 genes generated transformants that are easy to screen. Furthermore, the DAO1 gene can be readily used for either positive or negative selection, depending on the substrate, thus it provides a unique conditional, substrate-dependent positive/negative selectable marker for plant transformation. In summary, the presented work demonstrates that introducing the ability to catalyse a single metabolic step can allow plants to exploit an otherwise inaccessible or toxic form of organic nitrogen, and provides a versatile marker based on nitrogen nutrition for selecting transgenic plants. A possible role for D-serine in plants’ touch response is also reviewed in the thesis.
LibD: Scalable and Precise Third-Party Library Detection in Android Markets
(2017)

With the thriving of the mobile app markets, third-party libraries are pervasively integrated in the Android applications. Third-party libraries provide functionality such as advertisements, location services, and social networking services, making multi-functional app development much more productive. However, the spread of vulnerable or harmful third-party libraries may also hurt the entire mobile ecosystem, leading to various security problems. The Android platform suffers severely from such problems due to the way its ecosystem is constructed and maintained. Therefore, third-party Android library identification has emerged as an important problem which is the basis of many security applications such as repackaging detection and malware analysis. According to our investigation, existing work on Android library detection still requires improvement in many aspects, including accuracy and obfuscation resilience. In response to these limitations, we propose a novel approach to identifying third-party Android libraries. Our method utilizes the internal code dependencies of an Android app to detect and classify library candidates. Different from most previous methods which classify detected library candidates based on similarity comparison, our method is based on feature hashing and can better handle code whose package and method names are obfuscated. Based on this approach, we have developed a prototypical tool called LibD and evaluated it with an update-to-date and large-scale dataset. Our experimental results on 1,427,395 apps show that compared to existing tools, LibD can better handle multi-package third-party libraries in the presence of name-based obfuscation, leading to significantly improved precision without the loss of scalability.
Generating Single Subject Activity Videos as a Sequence of Actions Using 3D Convolutional Generative Adversarial Networks
(2017)

Humans have the remarkable ability of imagination, where within the human mind virtual simulations are done of scenarios whether visual, auditory or any other senses. These imaginations are based on the experiences during interaction with the real world, where human senses help the mind understand their surroundings. Such level of imagination has not yet been achieved using current algorithms, but a current trend in deep learning architectures known as Generative Adversarial Networks (GANs) have proven capable of generating new and interesting images or videos based on the training data. In that way, GANs can be used to mimic human imagination, where the resulting generated visuals of GANs are based on the data used during training. In this paper, we use a combination of Long Short-Term Memory (LSTM) Networks and 3D GANs to generate videos. We use a 3D Convolutional GAN to generate new human action videos based on trained data. The generated human action videos are used to generate longer videos consisting of a sequence of short actions combined creating longer and more complex activities. To generate the sequence of actions needed we use an LSTM network to translate a simple input description text into the required sequence of actions. The generated chunks are then concatenated using a motion interpolation scheme to form a single video consisting of many generated actions. Hence a visualization of the input text description is generated as a video of a subject performing the activity described.
SLAM-based automatic extrinsic calibration of a multi-camera rig
(2011)

Cameras are often a good choice as the primary outward-looking sensor for mobile robots, and a wide field of view is usually desirable for responsive and accurate navigation, SLAMand relocalisation. While this can potentially be provided by a single omnidirectional camera, it can also be flexibly achieved by multiple cameras with standard optics mounted around the robot. However, such setups are difficult to calibrate. Here we present a general method for fully automatic extrinsic auto-calibration of a fixed multi camera rig, with no requirement for calibration patterns or other infrastructure, which works even in the case where the cameras have completely non-overlapping views. The robot is placed in a natural environment and makes a set of programmed movements including a full horizontal rotation and captures a synchronized image sequence from each camera. These sequences are processed individually with a monocular visual SLAM algorithm. The resulting maps are matched and fused robustly based on corresponding invariant features, and then all estimates are optimised full joint bundle adjustment, where we constrain the relative poses of the cameras to be fixed. We present results showing accurate performance of the method for various two and four camera configurations.
Fusion of laserscannner and video based lanemarking detection for robust lateral vehicle control and lane change maneuvers
(2011)

The knowledge about lanes and the exact position on the road is fundamental for many advanced driver assistance systems. In this paper, a novel iterative histogram based approach with occupancy grids for the detection of multiple lanes is proposed. In highway scenarios, our approach is highly suitable to determine the correct number of all existing lanes on the road. Additionally, the output of the laserscannner based lane detection is fused with a production-available vision based system. It is shown that both sensor systems perfectly complement each other to increase the robustness of a lane tracking system. The achieved accuracy of the fusion system, the laserscannner and video based system is evaluated with a highly accurate DGPS to investigate the performance with respect to lateral vehicle control applications.